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Prime Electronics and Satellitics Inc. 13
May 30, 2024 08:37 adjust visualisation style, allow draw_bbox() to show person id May 28, 2024 06:08 Add method to free buffers May 23, 2024 05:24 Implement pycuda backend for inference with TensorRT engine in additon to original polygraphy backend.
the default is polygraphy. You can set TRT_BACKEND to 'PYCUDA' to choose pycuda backend. May 21, 2024 10:36 Add 'get_model_format_and_input_shape' helper function Luigi
06:07 Return and show bounding box confidence May 19, 2024 10:09 Deal with multiple cameras Luigi
May 17, 2024 15:31 Show bounding box on screen too Luigi
15:02 Internalize input frame buffer Luigi
14:15 Unify batched and non-batched versions Luigi
07:13 Support Inference over batch with TensorRT Engine Model 05:50 Use ONNXruntime instead of ONNX.checker.check_model to detect ONNX model as the latter is too strict, it sometimes rejects ONNX models May 16, 2024 10:12 Resize in keeping aspect ratio in visualization 09:51 fix postprocess_batch method in RTMO_GPU_Batch class May 15, 2024 03:35 Add model format (ONNX/Engine) & input size detection (based on file header, not on filename) for RTMO Apr 19, 2024 17:11 Add support TensorRT engine support for RTMO Mar 19, 2024 08:37 Load ./libmmdeploy_tensorrt_ops.so for TensorRT EP if available Mar 8, 2024 08:54 Add option --yolo_nas_pose, used to read YOLO NAS Pose model instead of RTMO Mar 7, 2024 08:23 Make ONNX models compatible with ONNXruntime's TensorrtExecutionProvider - Use FP16 ONNX Model with Batch Size 4 for DEMO with TRT EP
- Perform shape inference on all ONNX models
- Removed int8 models which is not recommended due to performance drop
- Add ONNX models with fixed input batch size 1,2,4
- Enable TensorRT Execution Provider (TRT EP) Mar 5, 2024 05:51 Add new class 'RTMO_GPU_Batch' that can perform inference on batch of images 05:13 Optimization for CUDAExecutionProvider - Use IO Binding & Enable Use MAX Workspace to Optimize for RTMO
- Eliminate the dependency on rtmlib Mar 1, 2024 10:14 RTMO ONNX Models from MMPOSE range from Tiny to Large 
